Automatic localization of cancer on whole-slide histology images from radical prostatectomy specimens would support quantitative, graphical pathology reporting and research studies validating in vivo imaging against gold-standard histopathology. There is an unmet need for such a system that is robust to staining variability, is sufficiently fast and parallelizable as to be integrated into the clinical pathology workflow, and is validated using whole-slide images. We developed and validated such a system, with tuning occurring on an 8-patient data set and cross-validation occurring on a separate 41-patient data set comprising 703,745 480μm × 480μm sub-images from 166 whole-slide images. Our system computes tissue component maps from pixel data using a technique that is robust to staining variability, showing the loci of nuclei, luminal areas, and areas containing other tissue including stroma. Our system then computes first- and second-order texture features from the tissue component maps and uses machine learning techniques to classify each sub-image on the slide as cancer or non-cancer. The system was validated against expert-drawn contours that were verified by a genitourinary pathologist. We used leave-one-patient-out, 5-fold, and 2-fold cross-validation to measure performance with three different classifiers. The best performing support vector machine classifier yielded an area under the receiver operating characteristic curve of 0.95 from leave-one-out cross-validation. The system demonstrated potential for practically useful computation speeds, with further optimization and parallelization of the implementation. Upon successful multi-centre validation, this system has the potential to enable quantitative surgical pathology reporting and accelerate imaging validation studies using histopathologic reference standards.
W. Han, C. Johnson, M. Gaed, J. A. Gomez, M. Moussa, J. L. Chin, S. E. Pautler, G. Bauman, and A. D. Ward, "Automatic cancer detection and localization on prostatectomy histopathology images ," Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810Q (Presented at SPIE Medical Imaging: February 12, 2018; Published: 6 March 2018); https://doi.org/10.1117/12.2292450.
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